Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Signal Processing

Background:

• Simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) offer combined temporal and spatial resolution for neuroscience research.
• EEG signals recorded during fMRI are susceptible to artifacts from electromagnetic fields, head motion, and the Hall phenomenon, compromising data integrity.

Purpose of the Study:

• To develop and validate a methodology for detecting and characterizing hidden artifacts within simultaneous EEG-fMRI recordings.
• To enhance the analysis of EEG data acquired during fMRI by addressing signal corruption.

Main Methods:

• A top-down strategy was implemented, utilizing time-domain independent component analysis (ICA) to extract independent components and spatial weights.
• Nonlinear dimension reduction via t-distributed stochastic neighbor embedding (t-SNE) was employed to create a low-dimensional data representation.
• Density-based spatial clustering of applications with noise (DBSCAN) was used to partition the reduced data space.

Main Results:

• The methodology successfully identified artifacts related to electrooculography (EOG), electrocardiography (ECG), electromyography (EMG), and fMRI gradient switching.
• Independent components and spatial weights derived from ICA provided insights into artifactual sources.
• Clustering in the t-SNE space revealed distinct data structures corresponding to different artifact types.

Conclusions:

• The proposed methodology effectively extracts information about various artifacts from simultaneous EEG-fMRI data.
• This approach can significantly aid in identifying and mitigating artifacts, thereby improving the reliability of combined EEG-fMRI studies.
• The technique offers a valuable tool for researchers seeking cleaner EEG data in the challenging fMRI environment.